Shockable heart rhythm classifier for defibrillators

ABSTRACT

Neural network based heart rhythm classifiers are described. The neural network is configured to receive an electrocardiogram segment and to output an indication of whether the electrocardiogram segment represents a heart rhythm that is suitable for treatment by a defibrillation shock. Preferably, the received electrocardiogram segment is not transformed or processed prior to its reception by the neural network and no features of the electrocardiogram are identified to the neural network. In some embodiments, the received electrocardiogram segment is the sole input to the neural network. In various embodiments, the neural network is configured to classify electrocardiogram segments obtained while CPR was being performed on the patient. Classifiers that output a characteristic of CPR are also described. Such outputs may include an indication of whether CPR was being performed while the ECG was being detected, compression depth, etc. The described classifiers are well suited for use in defibrillators.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. application Ser. No.16/568,030, which claims the priority of U.S. Provisional PatentApplication No. 62/731,255 filed Sep. 14, 2018, both of which areincorporated herein by reference in their entirety.

BACKGROUND

The present invention relates generally to systems for automaticallyclassifying cardiac rhythms to determine whether a patient has a heartrhythm that is appropriate for defibrillation shock therapy.

Defibrillators are devices that apply electric shock therapy to cardiacpatients experiencing abnormal heart rhythms that may be treated bydefibrillation shock therapy. There are several types of defibrillatorsthat are currently available. External defibrillators are most commonlyused in emergency situations in which a patient has suffered cardiacarrest, or where a cardiac arrest is imminent. External defibrillatorsinclude automated external defibrillators (AEDs) and manualdefibrillators. Manual defibrillators are generally intended to beoperated by trained emergency medical personnel or physicians andtypically give the operator a great deal of control over theadministration of a defibrillation shock. AEDs are typically designed tobe used by lay operator and therefore automate most or all of thedefibrillators functionality, including cardiac rhythm diagnosis anddeciding whether or not a defibrillation shock should be delivered. Ifthe AED is fully automated, the AED will deliver the defibrillationshock without requiring any input commands from a user. In contrast,when the AED is partially automated, the AED will typically inform anoperator that a shock is advised, but will require that the operatorpush a “shock” button in order to initiate the defibrillation shock.Internal (implanted) defibrillators are implanted into the chests ofpatients that are known to have cardiac issues that cannot be treated inother ways. Internal defibrillators typically operate in an automaticmode.

Regardless of their type, defibrillators typically includeelectrocardiogram (ECG) detection circuitry configured to obtain andoutput an ECG signal from electrodes connected to a patient. Forexternal defibrillators such as AEDs, the electrodes typically take theform of pads or paddles placed on the patient's chests. For implantabledefibrillators, the electrodes are connected internally—as for exampleby attachment directly to a patient's heart or at other locations deemedappropriate for defibrillation. The ECG signal outputted by the ECGdetection circuitry is then typically processed in various ways andthereafter passed to a rhythm detector or classifier which determineswhether the patient has a shockable heart rhythm. If a shockable rhythmis detected, the AED will deliver a defibrillation shock to the patient.

The two most common conditions treated by defibrillation shock therapyare Pulseless Ventricular tachycardia (aka VT or V-Tach) and Ventricularfibrillation (VF or V-Fib). The classifiers must be able to accuratelyidentify the presence of these types of rhythms as well as a variety ofless common cardiac rhythms that can benefit from a defibrillationshock. Just as importantly, they must be able to distinguish normalrhythms and abnormal rhythms such as normal sinus rhythm, atrialfibrillation, sinus blockage, supraventricular tachycardia,idioventricular contraction, premature ventricular contraction, andasystole for which defibrillation shocks are not advised.

Over the years a number of electrocardiogram classification schemes havebeen proposed and implemented for use in identifying shockable rhythms Afew such classification methods include frequency domain analysis,gradient pattern detection, waveform shape matching techniques andneural networks. One common classification approach is to identify(extract) selected features of the QRS portions of an ECG signal and tobase classification on those features. As understood by those familiarwith reading electrocardiograms, the recurring spikes in a ventricularwaveform from an electrocardiogram are known as the R waves. The Q and Swaves respectively, are the smaller inverted spikes on either side of anR wave. Typical QRS features of interest might include parameters suchas QRS width, amplitude, polarity, area, r-r interval, etc. Theseextracted features are submitted to a classifier which classifies theheart signal based on the extracted features. The classification resultsare then used to determine whether the detected cardiac rhythm is a goodcandidate for defibrillation shock therapy.

Although the existing classifiers work well, there are continuingefforts to further improve the accuracy of detection.

SUMMARY

A variety of neural network based shockable heart rhythm classifiers aredescribed. The neural network is configured to receive anelectrocardiogram segment as an input and to generate an outputindicative of whether the received electrocardiogram segment representsa heart rhythm that is suitable for treatment by a defibrillation shock.Preferably, the received electrocardiogram segment is not transformed orprocessed prior to its reception by the neural network and no featuresof the electrocardiogram are identified to the neural network. In someembodiments, the received electrocardiogram segment is the sole input tothe neural network. In some embodiments the classifier is trained toidentify shockable heart rhythms in electrocardiogram segments obtainedwhile a patient is receiving cardio-pulmonary resuscitation.

In some embodiments, the neural network has an input, at least twohidden layers and an output layer. The input may be a raw detectedelectrocardiogram segment. In some embodiments, the input is received asan array of samples obtained at a sampling frequency in the range of 100to 600 samples per second on an electrocardiogram segment having alength of less than 15 seconds.

In some embodiments, the convolutional neural network includes in therange of 2-6 hidden layers. In some embodiments, each hidden layerincludes in the range of 8 to 32 filters. In some embodiments, eachfilter has a filter size in the range of 3-16 and a stride rate in therange of 1-5.

In some embodiments, the output of the classifier is a numericprobability value indicative of a determined probability that thereceived electrocardiogram segment represents a heart rhythm that issuitable for treatment by a defibrillation shock.

In another aspect, neural network based classifiers that output aparameter indicative of CPR are described. The neural network isconfigured to receive an electrocardiogram segment as an input and togenerate an output parameter indicative of CPR performed on the patient.Preferably, the received electrocardiogram segment is not transformed orprocessed prior to its reception by the neural network and no featuresof the electrocardiogram are identified to the neural network. In someembodiments, the received electrocardiogram segment is the sole input tothe neural network. In some embodiments the classifier is trained toidentify shockable heart rhythms in electrocardiogram segments obtainedwhile a patient is receiving cardio-pulmonary resuscitation.

In some embodiment, the output classification is or includes anindication of whether CPR was being performed on the patient duringdetection of the electrocardiogram segment. In some embodiments, theoutput classification is or includes a parameter indicative of CPRcompression depth or compression rate. In some embodiments, theclassifier is further configured to output a second classificationindicative of whether the received electrocardiogram segment representsa heart rhythm that is suitable for treatment by a defibrillation shock.

In various embodiments, the neural network shockable rhythm classifieris incorporated into a defibrillator capable of deliveringdefibrillation shock therapy. In some embodiments the defibrillator isconfigured to provide one or more instruction(s) or feedback to a userbased at least in part on the output classification.

In various embodiments, the instruction(s) or feedback include one ormore of: an instruction to stop or begin CPR compressions; aninstruction to push harder or reduce the pressure during CPRcompressions; or an instruction to increase or decrease the frequency ofCPR compressions.

Methods of training the described neural networks are also described.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention and the advantages thereof, may best be understood byreference to the following description taken in conjunction with theaccompanying drawings in which:

FIGS. 1(a) and 1(b) are block diagrams illustrating a convolutionalneural network based shockable rhythm classifier in accordance with oneembodiment.

FIG. 2 is a block diagram of an ECG sensing and classifying system thatincorporates a neural network based shockable rhythm classifier.

FIG. 3 is a timing diagram showing an ECG segment suitable for use bythe convolutional neural network based shockable rhythm classifier ofFIGS. 1(a) and 1(b).

FIG. 4 is a block diagram of an electronics architecture of an automatedexternal defibrillator (AED) that incorporates an ECG sensing andclassifying system.

In the drawings, like reference numerals are sometimes used to designatelike structural elements. It should also be appreciated that thedepictions in the figures are diagrammatic and not to scale.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention relates generally to a neural network shockablerhythm classifier that is configured to identify cardiac rhythms thatare suitable for defibrillation shock therapy. The described classifieris well suited for use in defibrillators to identify shockable heartrhythms in real time. Referring next to FIG. 1(a), a shockable rhythmclassifier 200 in accordance with one embodiment will be described. Theclassifier 200 is convolutional neural network based classifier. In theillustrated embodiment the neural network includes an input layer 201,three convolutional neural net hidden layers 202 and an output layer203. In other embodiments, more or fewer convolutional (hidden) layersmay be used. Minimally a single hidden layer may be used—althoughtypically, a minimum of two or three hidden layers is preferred.Typically is it not desirable to provide too many layers so that themodel does not simply memorize examples instead of actually learninguseful features about the data. Additionally, there is a point whereadding more layers may provide trivially small improvements such thatextra computation is not justified. Therefore, in general, two to sixconvolutional layers are believed to work well.

In the embodiment illustrated in FIG. 1(a), the sole input to theclassifier 200 is a segment of an ECG signal having a designated length.The output of the classifier is a weight or a set of weights indicatingthe classifier's confidence level that the inputted segment constitutesa shockable rhythm. This output is sometimes referred to herein as shockclassification 204. The designated length of the ECG segment analyzedmay vary based on the needs of the system used. In general, the ECGsegment length should be long enough so that the segment can be reliablyanalyzed, but short enough so that analysis is not unduly delayed. Thisis important because defibrillators are typically used in life savingsituations and extended delays in obtaining or processing the ECGsignals that are analyzed can delay the administration of a shock whenneeded, which can adversely impact the probability of defibrillationbeing successful. In some embodiments, ECG segment lengths on the orderof 4 to 15 seconds long are used although in different embodimentseither shorter or longer segments may be used. By way of example, insome specific implementations, ECG segments less than 10 seconds long,as for example, four (4) to eight (8) second long ECG segments have beenfound to work well.

FIG. 1(b) is another diagrammatic illustration of the shockable rhythmclassifier 200 which highlights the nature of the input layer and theconvolutional nature of the hidden layers 202(a), 202(b) and 202(c). Inthe illustrated embodiment, the input is a 1-dimensional vector. Morespecifically, the input may take the form of a linear array of ECGsamples corresponding to the designated segment length.

The specific sample rate used may vary, but sample rates on the order of100 to 600 samples per second are believed to be suitable. The value ofeach sample may correspond to the amplitude of the ECG signal in thecorresponding sample period—as for example in millivolts. The totalnumber of samples in the input array is the product of the sample rate(samples/sec) times the segment length (seconds).

In each convolutional layer 202, a convolutional kernel is strided overthe previous layer or set of high-level features, which can beunderstood as a sliding-window that applies a dot-product between theinput and the kernel. After each layer is computed, the next layercontains a set of high-level features that represent patterns andcombinations of patterns that were found by the convolutional kernels. Aconvolutional kernel represents a pattern and if a matching pattern isfound in the input, then the dot-product will return a higher value,thus passing that information on to the next layer to form a high-levelfeature. The last convolutional layer output is flattened into a 1dimensional vector 203. And finally a linear transformation is appliedto each of the values in the flattened layer and the final scalar value204 is the outputted value. The outputted value may be a value between 0and 1, where a value greater than or equal a designated thresholdrepresents a shockable input signal and a value less than the designatedthreshold represents a non-shockable input signal.

A noteworthy feature of the classifier 200 is that it is purely datadriven based on raw ECG segments that have not undergone typicalanalysis such as wavelet analysis, beat to beat analysis, or QRSdetection analysis. It should be appreciated that the ECG samples willoften have gone through some relatively minimal level of signalfiltering by the ECG sensing circuitry to reduce noise (e.g. low or highpass filters), etc. but no ECG feature extraction. That is, the input tothe classifier is a segment of the ECG signal outputted by ECG sensingcircuitry itself (which is sometimes referred to herein as the raw orunprocessed ECG signal). No effort is made to extract features of theECG prior to classification, and/or to utilize such extracted featuresas inputs to the classifier as has often been done in prior artclassifiers, including prior art neural network based classifiers (e.g.,wavelet transforms). Similarly, no effort is made to transform the ECGsignal prior to submission to the classifier as is common in a varietyof other prior art classifiers, including some prior art neural networkbased classifiers.

Although feature extraction and/or signal transformation can be usefulfor many prior art classifiers, we believe that better classificationresults can be attained using a deep learning neural network that istrained based on raw or minimally processed ECG segments. This isbecause, as a practical matter, extracted features cannot fully define arhythm as complex as an ECG of a patient experiencing an arrhythmia ortachycardia. Similarly, as a practical matter, any signal transformationwill inherently result in the loss of some information carried in theoriginal signal—which inherently limits the performance of a classifier.Therefore, a classifier that takes in an unprocessed signal cantheoretically achieve equal or better performance than one that has hadprocessing done prior to being fed to the classification module. Moderndeep learning based neural networks such as the described convolutionalneural network are well suited for handling complex inputs and thecomplexities of an raw ECG signal are well within the capabilities ofmodern deep machine learning tools. Furthermore, as discussed in moredetail below, the use of the raw or substantially unprocessed ECG signalallows the classifier to analyze the ECG segments for characteristics ofinterest that are not part of conventional arrhythmia classificationsuch as patient gender, age or other patient properties that may bereflected in the ECG but may be obscured or lost in the pre-processingrequired by conventional classifiers.

It is noted that many ECG detection circuits do some minimal level offiltering of the ECG signal to reduce noise. The output of such ECGdetection circuits is considered to be an unprocessed/raw ECG signal inthe context of this disclosure.

The neural network classifier 200 is generated using deep machinelearning trained with error back propagation. As will be appreciated bythose familiar with machine learning, it is generally desirable to trainthe classifier with a training set having a large number of samplesincluding a number of samples of each type of rhythm that the classifiermight be expected to encounter and classify. The training data setpreferably includes a large number of each type of rhythm of interestincluding normal rhythms, unusual rhythms of different types that arenot shockable rhythms, and every type of rhythm for which defibrillationshock therapy is desired. In general, the quality of the classifier'sresults will improve when larger training sets are used that include alarge number of samples of each rhythm type of interest. An advantageousfeature of neural network based classifiers is that as more trainingdata becomes available, the classifier can be updated by retraining toincorporate the most recent information available.

The segments utilized in training have the same segment length as thesegments that will be analyzed by the classifier and the trainingsegments are input in the same format as the samples that will beanalyzed. Thus, the training segments are inputted as an array ofsamples that are sampled at a designated sampling rate and correspond tothe designated sample length.

The training of the neural network classifier is automated in that theneural network trains itself based on the data it receives and the backpropagation of errors identified in the training. The automated trainingdefines the set of weights attributed to each link between nodes. Theclassifier is trained using raw ECG segments that are the same length asthe segments to be handled by the classifier. For example, if 8 secondsegments are to be used as the inputs to the classifier, then thetraining is based on 8 second segments (although as mentioned above, theactual segment length used may vary in accordance with the design goalsand needs of any particular implementation). Each of the segments usedin training is known to correspond to either a shockable rhythm or arhythm that is not suitable for treatment by administering adefibrillation shock. The back propagation of errors identified duringthe training is particularly helpful to establishing a robust and highlyaccurate model.

The neural network classifier 200 may be created by any appropriateneural network generator—as for example, TensorFlow™ which is an opensource machine learning framework and software library originallydeveloped and made publically available by Google. Classifiers createdusing TensorFlow™ are well suited for use in handling the describedunprocessed/raw ECG signal.

As will be appreciated by those familiar with convolutional neuralnetworks, a good way to specify a convolutional neural network layer isto specify the number of convolutional layers, the number of filters,the filter (kernel) size and the stride rate. The specific values forthe number of convolutional layers, filters, filter size and stride ratemay vary widely based on the needs of any particular system. Forexample, it is believed that in the range of 2 to 6 convolutional layershaving on the order of 8-32 filters, filter sizes on the order of 3-16and a stride rate in the range of 1-5 are generally suitable fordefibrillation heart rhythm classification. Of course, these values aresomewhat dependent on one another.

As previously mentioned, the input to the convolutional neural networkmay be a linear array of ECG samples. The specific sample rate used mayvary, but sample rates on the order of 100 to 600 samples per second asfor example 250 samples per second are believed to be suitable.

In the first described embodiment, the output of the classifier 200 issimply a value indicative of whether the received ECG reflects a cardiacrhythm that the classifier believes can be treated by defibrillationshock therapy. The outputted value may be a simple shock/no shockdecision, or it may be a numeric probability value indicative of theprobability that the detected rhythm is a shockable rhythm. When theoutput of the classifier is a numeric probability value, thedefibrillator controller may have a defined threshold probability whichif met or exceeded, is treated as a shockable rhythm.

In some embodiments, the classifier does not output an identification ofthe specific type of shockable rhythm that is detected (e.g., no effortis made to identify whether the detected shockable rhythm was V-Tach orV-Fib or some other type of shockable rhythms) For an AED, this is oftensufficient because the AED itself only needs to know whether or not ashock should be delivered and a diagnosis of the particular type ofrhythm detected is less important—especially if the detectedelectrocardiogram is stored so that it can be presented or transmittedto emergency and/or medical personnel who can analyze the ECG itself.

In other embodiments, it may be helpful for the classifier to alsooutput a diagnosis of the type of rhythms detected. This can readily bedone by reconfiguring the output layer of the neural networkappropriately. For example, the classifier can readily be configured andtrained to output a classification of the analyzed rhythm (e.g., normal,V-Tach, V-Fib, etc.) in addition to, or in place of the simple shockdecision. Again, the output can be characterized in terms of a numericprobability instead of simply the classification if/when desired. Theprobability associated with the analyzed rhythm diagnosis can be helpfulto both (a) responding and/or treating medical personnel as an indicatorof the patient's condition; and (b) to facilitate analysis of theefficacy of the defibrillator or the classifier itself and potentiallyto help facilitate future training of the classifier.

Referring next to FIG. 2, an ECG handling system 205 will be described.The ECG handling system 205 includes an ECG sensor/detector 225,electrodes 116, ECG parser 230 and convolutional neural network basedshockable rhythm classifier 200. The ECG sensor 225 is arranged todetect electrical cardiac signals picked up by electrodes 116 and tooutput an electrocardiogram signal 227 in a conventional manner. The ECGsensor/detector 225 typically does some basic filtering, but is intendedto output a standard ECG signal—which is sometimes referred to herein asan unprocessed/raw ECG signal. That is, the sensor/detector 225 does notdo any special processing, feature extraction, or transformation of theECG signal.

It should be appreciated that the basic ECG sensor/detector outputfiltering commonly includes some low pass filtering; high passfiltering; DC offset filtering; and/or notch filtering. The high and lowpass filtering are designed to eliminate noise in frequency rangesoutside of frequencies relevant to the ECG itself (i.e. above and belowrelevant ECG frequencies). The DC offset filtering is intended tonormalize the signal around a designated reference (e.g., a mean of 0 mVas opposed to a mean of 3 mV). The notch filter is intended to filterout expected noise—as for example filtering out the 50 Hz or 60 Hz noisethat exists in AC power supplies in Europe and the United Statesrespectively). As will be appreciated by those familiar with the art,all of these would be considered good practice filtering of the outputof an ECG sensor/detector and are not designed to extract or highlightany features or characteristics of the ECG signal itself. As mentionedabove, the unprocessed/raw ECG signal utilized as the input to theclassifier will typically have been subjected to this type of basic ECGsensor/detector output filtering.

The electrodes 116 may take the form of any electrodes suitable fordetecting a patient's ECG. For example, in the context of an AED, theelectrodes 116 may be standard external defibrillator electrode pads orpaddles. In the context of an implanted defibrillator, the electrodes116 may take the form of conventional internal defibrillator electrodeleads.

The electrocardiogram signal 227 is fed to ECG parser 230 which forwardsappropriate length segments 233 of the electrocardiogram signal 227 tothe shockable rhythm classifier 200 as described above. In someembodiments, the parser 230 is arranged to forward ECG segments 233 tothe shockable rhythm classifier at regular intervals. The specificintervals at which updates are sent may be widely varied to meet theneeds and capabilities of any particular implementation. By way ofexample, update intervals on the order of 0.1 to 2 seconds work well inmany implementations, although either longer or shorter update intervalsmay be appropriate for specific implementations. Thus, for example, if 8second long segments are sent the classifier every 0.2 seconds, eachsample sent by the parser 230 would contain the last eight seconds ofECG signal. In this way, the ECG segment sent to the classifier iseffectively indexed 0.2 seconds each sample. For each sample, theshockable rhythm classifier identifies whether the sample is perceivedto represent a shockable rhythm and that determination (shockclassification 204) may be sent to a defibrillator controller 130 whichtakes the appropriate actions based on the current circumstances,including the shock classification determined by shockable rhythmclassifier 200.

It is noted that the classifier can continue to monitor the ECG rhythmand report its analysis even after a shock decision has been made by thedefibrillator controller. Therefore, if there is a sudden change in theECG after a shock decision has been made but before a shock is actuallydelivered (e.g., a change from V-Fib to a normal sinus rhythm (NSR)),then the defibrillator controller can decide not to deliver a shock.

An example of a shifted, real time series of ECG segments passed fromthe parser to the classifier 200 is illustrated in FIG. 3. FIG. 3 showsan ECG reading 227 at a given time. The parser 230 takes the last 8seconds of the ECG signal and passes that segment, to the shockablerhythm classifier. After a designated interval (e.g. 0.2 seconds),another sample will be sent to the classifier. Through the delay, theECG sensor continues to detect and output more of the ECG. When it istime to send the next sample, the parser will send the last 8 seconds ofthe EGG segment that it then has. This process is repeated as long asclassification is desired and the system keeps receiving usable ECGsignal. When the sample interval is short compared to the sample length,the majority of the signal sent to the classifier will be overlappingwith the addition of the portion of the ECG signal that has beenreceived since the last sample and the truncation of the portion of theECG signal that is older than the sample length.

The described shockable rhythm classifier may be incorporated into adefibrillator of any type—including automated external defibrillators(AEDs), manual external defibrillators, wearable defibrillators,implantable defibrillators, etc. By way of example, a couple specificAED designs that can benefit from the described shockable rhythmclassifier are described in applicant's U.S. Pat. No. 10,029,109 filedDec. 7, 2017 and patent application No. 62/674,711 filed May 22, 2018and Ser. No. 16/145,657 file Sep. 28, 2018, each of which isincorporated herein by reference. Of course, the described shockablerhythm classifier may be incorporated into a wide variety of otherdefibrillator designs as well.

In many embodiments, the defibrillator will store the entire detectedECG signal either persistently or transitorily. The stored ECG can beprovided to responding emergency personnel and/or medical personnel toprovide a better understanding of the incident.

The shockable rhythm classifier and associated ECG handling may also beimplemented in a wide variety of different manners. In some embodiments,the classifier may be implemented on a dedicated neural networkprocessor (NNP), such as Google's Tensor Processing Unit (TPU) orIntel's Neural Network Processor. In other embodiments, the shockablerhythm classifier may be implemented in or as a software module executedon a processor or microcontroller such as defibrillator controller 130.The functionality of the ECG parser may also be performed by the neuralnetwork processor, of if preferred, the defibrillator controller. Inother embodiments, parser 230 and/or the shockable rhythm classifier 200may be implemented as one or more separate components having its/theirown processor(s). In still other embodiments, the shockable rhythmclassifier and/or the parser 230 may be implanted in discrete orprogrammable logic as may be appropriate for any particularimplementation.

FIG. 4 is an electrical block diagram illustrating an automated externaldefibrillator architecture that incorporates the described ECG handlingmechanism 205. In the illustrated embodiment, the electrical componentsof defibrillator unit 110 include a defibrillator controller 130, memory133, a charging power regulator 140, a voltage booster 145 (which mayhave multiple stages), a high voltage capacitor 150 for temporarilystoring sufficient electrical energy suitable to provide adefibrillation shock, discharge control circuitry 160, pad relatedsensing circuitry 162 and relays 169, power storage unit 170, batteryregulator 193, status indicator(s) 175, speaker(s) 180, shockable rhythmclassifier 200 and one or more electrical connectors (e.g., interfaceconnector 190, mobile connector port 195, charger connector 197, etc).The charging power regulator 143 and voltage booster 145 which cooperateto control the charging of the shock discharge capacitor 150, aresometimes referred to herein as a charging circuit.

The defibrillator controller 130 is configured to control the operationof the base defibrillator unit and to direct communications withexternal devices, as appropriate. In some embodiments, the defibrillatorcontroller includes a processor arranged to execute software or firmwarehaving programmed instructions for controlling the operation of the baseunit, directing interactions with a user and communications withexternal components. As suggested above, in some embodiments, the ECGparser and the shockable rhythm classifier 200 may take the form ofsoftware modules executed on such a processor.

The defibrillator unit 110 may optionally be configured so that it iscapable of drawing power from certain other available power sourcesbeyond power storage unit 170 to expedite the charging of shockdischarge capacitor 150. The charging power regulator 140 is configuredto manage the current draws that supply the voltage booster, regardlessof where that power may originate from. For example, in someembodiments, supplemental power may be supplied from a mobile devicecoupled to mobile connector port 195 or from a portablecharger/supplemental battery pack coupled to charger connector 197.

The voltage booster 145 is arranged to boost the voltage from theoperational voltage of power storage unit 170 to the desired operationalvoltage of the discharge capacitor 150, which in the describedembodiment may be on the order of approximately 1400V-2000V (althoughthe defibrillator may be designed to attain any desired voltage). Insome embodiments, the boost is accomplished in a single stage, whereasin other embodiments, a multi stage boost converter is used. A fewrepresentative boost converters are described in the incorporated '835patent application. By way of example, in some embodiments, a flybackconverter, as for example, a valley switching flyback converter may beused as the voltage booster 145—although it should be appreciated thatin other embodiments, a wide variety of other types of voltage boosterscan be used.

A voltage sensor 151 is provided to read the voltage of the capacitor150. The voltage sensor 151 may take the form of a voltage divider orany other suitable form. This capacitor voltage reading is utilized todetermine when the shock discharge capacitor 150 is charged suitably foruse. The sensed voltage is provided to controller 130 which determineswhen the capacitor 150 is charged sufficiently to deliver adefibrillation shock. The capacitor 150 can be charged to any desiredlevel. This can be useful because different defibrillation protocolsadvise different voltage and/or energy level shocks for differentconditions. Furthermore, if the initial shock is not sufficient torestart a normal cardiac rhythm, some recommended treatment protocolscall for the use of progressively stronger impulses in subsequentlyadministered shocks (up to a point).

The discharge circuitry 160 may take a wide variety of different forms.In some embodiments, the discharge circuitry 160 includes an H-bridgealong with the drivers that drive the H-bridge switches. The drivers aredirected by defibrillator controller 130. The H-bridge outputs abiphasic (or other multi-phasic) shock to patient electrode pads 116through relays 169. The relays 169 are configured to switch between anECG detection mode in which the patient electrode pads 116 are coupledto the pad related sensing circuitry 162, and a shock delivery mode inwhich the patient electrode pads 116 are connected to H-Bridge tofacilitate delivery of a defibrillation shock to the patient. Althoughspecific components are described, it should be appreciated that theirrespective functionalities may be provided by a variety of othercircuits.

The pad related sensing circuitry 162 may include a variety of differentfunctions. By way of example, this may optionally include a padconnection sensor 164, an impedance measurement filter 166, and ECGsensor/detector circuitry 225. The pad connection sensor is arranged todetect the pads are actually connected to (plugged into) the basedefibrillator unit 110. The ECG sensor/detector circuitry 225 senseselectrical activity of the patient's heart when the pads are attached toa patient and outputs the electrocardiogram signal 227 to the shockablerhythm classifier 200 (via parser 230) for analysis to determine whetherthe detected cardiac rhythm indicates a condition that is a candidate tobe treated by the administration of an electrical shock (i.e., whetherthe rhythm is a shockable rhythm) and the nature of the recommendedshock. When the shockable rhythm classifier 200 is not integrated withthe defibrillator controller 130, the electrocardiogram signal 227 mayalso be passed to the defibrillator controller 130 which stores thedetected rhythm in memory so that it can be shown or sent to emergencymedical personnel if/when desired. When a shockable rhythm is detected,the controller 130 directs the user appropriately and controls the shockdelivery by directing the H-bridge drivers appropriately.

In some embodiments, the power storage unit 170 takes the form of one ormore rechargeable batteries, although other power storage devices suchas one or more supercapacitors, ultracapacitors, etc. and/orcombinations thereof may be used as deemed appropriate for anyparticular application. In some embodiments, the defibrillator unit 110is capable of drawing power from other available power sources for thepurpose of one or both of (a) expediting the charging of shock dischargecapacitor 150 and (b) recharging the power storage unit 170. In someembodiments, the battery can be recharged using one or more of theexternally accessible connector ports 195, a dedicated charging station,a supplemental battery pack, an interface unit, etc. When wirelesscharging is supported, the base defibrillator unit may include awireless charging module 174 configured to facilitate inductive chargingof the power storage unit 170 (e.g. using an inductive charging station,or other devices that support inductive charging, as for example aninductively charging battery pack, a cell phone with inductive chargingcapabilities, etc.).

The defibrillator unit 110 also includes a number of software orfirmware control algorithms installed in memory 133 and executable onthe defibrillator controller 130. The control algorithms have programmedinstructions suitable for controlling operation of the defibrillator andfor coordinating communications between the defibrillator unit 110 andany connected or remote devices 105. These control routines include (butare not limited to): capacitor charge management algorithms for managingthe charging of the discharge capacitor; capacitor discharge managementalgorithms for managing the delivery of a shock as necessary; userinterface management algorithms for managing the user instructions givenby the defibrillator and/or any connected user interface devices duringan emergency; battery charge control algorithms for managing thecharging of power storage unit 170 and routing charging power to otherconnected components; testing and reporting algorithms for managing andreporting self-testing of the base unit; software update controlalgorithms and verification files that facilitate software updates andthe verification of the same. When the shockable rhythm classifier 200and parser 225 are implemented algorithmically, the control algorithmscan also include those modules.

CPR Artifacts

AEDs are typically used when it is suspected that a person might beexperiencing cardiac arrest. Cardiac arrest treatment protocolstypically call for defibrillation in conjunction with CPR to revive thepatient. An issue encountered by many existing external defibrillator isthat they cannot reliably classify ECGs that are obtained while CPR isbeing performed because the CPR compressions distort the detected ECGsignals significantly. Therefore, many AEDs instruct the rescuer to stopCPR for a period to allow the defibrillator to analyze the patient'sheart rhythm to facilitate the determination of whether a defibrillationshock is advised.

In some embodiments, the neural network classifier 200 is trained usingonly “clean” ECG segments that do not have any CPR artifacts (i.e., werenot obtained while CPR was being performed). If such a classifier isused in an AED or other defibrillator expected to be used in emergencysituations where CPR might be expected, it may be desirable to instructthe user to stop CPR for a brief period to attain a clean ECG signal tobe used by the classifier, much like most current defibrillators do.

In other embodiments, the neural network classifier 200 may be trainedto identify shockable rhythms that are detected while CPR is beingperformed. This can readily be accomplished by including a large varietyof different types of ECG segments that were obtained while CPR wasbeing performed in addition to using clean ECG segments as in theprevious embodiments. This can be a powerful enhancement since itdoesn't require a user to stop giving CPR as appropriate while theclassifier 200 determines whether the patient has a shockable rhythm. Achallenge to training the classifier in this manner is the lack ofpublic available data sets for training that categorize rhythms obtainedwhile CPR was being performed. However, as more such data becomesavailable, the classifier can be trained (and retrained) to identifyshockable rhythms regardless of whether CPR is being performed whileobtaining the ECG signal.

CPR Compression Feedback

As discussed above, CPR is typically recommended in conjunction withdefibrillation as a preferred treatment for cardiac arrest. Therefore,many AEDs include prompts to encourage responders to perform CPR and/orinstructions regarding how to perform CPR. Given the importance of CPR,some AEDs go so far as to include a chest compression detector(typically accelerometer based) attachment that can detect chestcompression. The detected chest compressions can then be analyzed andappropriate feedback can be given to the user (e.g., press harder,reduce pressure, compress at a slower or faster rate, etc.).

In some embodiments, the neural network shockable rhythm classifier 200or a parallel neural network CPR classifier (not shown) can be trainedto also provide indications about the depth or efficacy of chestcompression, and/or the rate of, CPR compressions. Such information canbe used by the AEDs processor to provide real time feedback to the userabout any chest compressions being administered. This information can beused for other purposes as well. For example, if the AED knows that arescuer is touching the patient, it may defer shock delivery until therescuer takes their hand of the patient to avid inadvertently shockingthe rescuer.

Other Types of Classification

It has been demonstrated in the literature that there are somevariations in ECG readings associated with some particular arrhythmiasthat vary in accordance with the patient's gender. Knowledge of thepatient's gender can be useful in a variety of applications including:facilitating the generation of gender tailored operator instructions;when reporting the incident to first responders and/or medicalpersonnel; potentially tailoring the shock treatment itself; etc. Anadvantage of using the unprocessed/raw ECG samples is that in someembodiments, with the availability of sufficient ECG samples fortraining, the classifier can be trained to report a prediction of thepatient's gender, along with a confidence level associated with theprediction. That gender prediction can then be used by the defibrillatorcontroller in any manner deemed appropriate.

One potential use case for using gender information is in tailoring theuser instructions in gender relevant manners. For example, recommendeddefibrillation practices often call for the removal of all clothing overthe chest and the defibrillator may issue an audio prompt and/or displayinstructions instructing a user to remove all clothing over thepatient's chest. Studies have shown that AED users that are notmedically trained are often reluctant to remove the bra of femalepatients for cultural reasons, which can hinder the efficacy of thedevice. When the gender of the patient is known or suspected by thedevice to be female, the clothing removal prompt can be modified toemphasize that if the patient is wearing a bra, the bra should beremoved as well.

In another example, if the detected ECG suggests that the patient ismale (or that the patient is more likely to have a relatively hairychest), the user instructions may be modified to emphasize that if thepatient has a hairy chest, it is important to shave the regions wherethe electrode pads are applied.

The recommended shock protocol can also vary with the age/size of thepatient. For example, it is well established that pediatric patientsrespond better to lower energy defibrillation shocks as compared withadult patients (E.g. 150 J shocks for adult vs. 50 J shocks forpediatric). A challenge encountered by current AEDs is distinguishingpatient age and size before delivering defibrillation therapy. Currentdefibrillators rely upon the operator (who may be a bystander respondingto the SCA incident) to decide whether the patient is classified aspediatric (e.g., under 8 years old or under 55 lbs) by either selectingpediatric defibrillation electrode pads which attenuate the shock to bea lower energy shock, or by pressing a button or otherwise indicatingthat the patient is a child. Requiring an untrained operator to decidewhether a patient should be treated as a pediatric patient vs. an adultincreases the risk of misclassification of the patient.

An advantage of using the unprocessed/raw ECG samples is that in someembodiments, with the availability of sufficient ECG samples fortraining, the classifier can be trained to report a prediction of thepatient's age and/or size, along with a confidence level associated withthe prediction. That age/size prediction can then be used by thedefibrillator controller in any manner deemed appropriate. For example,if the classifier has confidence that the patient qualifies fortreatment as a pediatric patient, the defibrillator controller canprompt the user to utilize pediatric pads if the patient is a child toinsert the appropriate electrode pads, or perform other actionsparticular to a pediatric patient, for instance performing lowercompression CPR. With high enough confidence, the classifier outputcould also be used by the defibrillator controller to adjust the energyof the defibrillation shock depending on the inferred patientproperties. For instance if the classifier was 90% confident that thepatient was under 55 lbs and under 8 years of age, the defibrillatorcould automatically reduce the defibrillation shock from say 150 J to 50J, and further instruct the user to place the electrode pads accordingto proper pediatric patient, and further perform CPR in accordance withpediatric CPR instead of adult CPR.

Other Features

Although only a few embodiments of the invention have been described indetail, it should be appreciated that the invention may be implementedin many other forms without departing from the spirit or scope of theinvention. Therefore, the present embodiments should be consideredillustrative and not restrictive and the invention is not to be limitedto the details given herein, but may be modified within the scope andequivalents of the appended claims.

What is claimed is:
 1. A shockable heart rhythm classifier comprising aneural network configured to receive a detected electrocardiogramsegment as an input and to generate an output classification indicativeof whether the received electrocardiogram segment represents a heartrhythm that is suitable for treatment by a defibrillation shock, whereinthe received electrocardiogram segment contains CPR artifacts indicativeof cardio-pulmonary resuscitation (CPR) being performed during thedetection of the electrocardiogram segment.
 2. A shockable heart rhythmclassifier as recited in claim 1 wherein the neural network is aconvolutional neural network.
 3. A shockable heart rhythm classifier asrecited in claim 1 wherein the received electrocardiogram segment is araw electrocardiogram segment.
 4. A shockable heart rhythm classifier asrecited in claim 1 wherein the raw electrocardiogram segment is the soleinput to the neural network.
 5. A shockable heart rhythm classifier asrecited in claim 1 wherein no features of the received electrocardiogramare identified to the neural network.
 6. A shockable heart rhythmclassifier as recited in claim 2 wherein the convolutional neuralnetwork has in the range of two to six convolutional layers.
 7. Ashockable heart rhythm classifier as recited in claim 6 wherein eachlayer includes in the range of 8 to 32 filters.
 8. A shockable heartrhythm classifier as recited in claim 7 wherein each filter has a filtersize in the range of 3-16 and a stride rate in the range of 1-5.
 9. Ashockable heart rhythm classifier as recited in claim 1 wherein thereceived electrocardiogram segment is an array of samples obtained at asampling frequency in the range of 100 to 600 samples per second andhaving a length of less than 15 seconds.
 10. An automated externaldefibrillator (AED) comprising a shockable heart rhythm classifier asrecited in claim 1, the AED unit further comprising: a capacitor unitcapable of temporarily storing and discharging sufficient energy todeliver a defibrillation shock to a patient via electrode padsconfigured to be attached to a patient; shock delivery circuitry fordischarging the capacitor unit to deliver the defibrillation shock;defibrillation electrode pads; electrocardiogram circuitry for detectingelectrocardiogram segment via the defibrillation electrode pads when thedefibrillation electrode pads are attached to a patient.
 11. A heartrhythm classifier comprising a neural network configured to: receive apatient's detected electrocardiogram segment as an input; and generatean output classification indicative of cardio-pulmonary resuscitation(CPR) performed on the patient while the electrocardiogram segment wasbeing detected.
 12. A heart rhythm classifier as recited in claim 11wherein the output classification is or includes an indication ofwhether CPR was being performed on the patient during detection of theelectrocardiogram segment.
 13. A heart rhythm classifier as recited inclaim 11 wherein the output classification is or includes a parameterindicative of CPR compression depth.
 14. A heart rhythm classifier asrecited in claim 11 wherein the output classification is or includes aparameter indicative of CPR compression rate.
 15. A heart rhythmclassifier as recited in claim 11 further configured to output a secondclassification indicative of whether the received electrocardiogramsegment represents a heart rhythm that is suitable for treatment by adefibrillation shock.
 16. A heart rhythm classifier as recited in claim15 wherein the electrocardiogram segment contains CPR artifactsindicative of CPR being performed during the detection of theelectrocardiogram segment.
 17. An automated external defibrillator (AED)that includes the heart rhythm classifier of claim 11, wherein thedefibrillator is configured to provide an instruction or feedback to auser based at least in part on the output classification.
 18. An AED asrecited in claim 17 wherein the instruction or feedback includes aninstruction to stop or begin CPR compressions.
 19. An AED as recited inclaim 17 wherein the instruction or feedback includes an instruction topush harder or reduce the pressure during CPR compressions.
 20. An AEDas recited in claim 17 wherein the instruction or feedback includes aninstruction to increase or decrease the frequency of CPR compressions.21. A method of training a shockable rhythm classifier comprising:providing a neural network; training the neural network to identifyshockable cardiac rhythms using a multiplicity of electrocardiogramsegments of a designated length that contain CPR artifacts as inputs,wherein each electrocardiogram segment is known to correspond to either(a) a shockable cardiac rhythm deemed to be representative of a cardiacrhythm that is suitable for treatment by administration of adefibrillation shock, or (b) a cardiac rhythm that is not deemed to berepresentative of a cardiac rhythm that is suitable for treatment byadministration of a defibrillation shock; andas  part  of  the  training, back  propagating  errors  that  are  identified  during  the  training.22. A method as recited in claim 21, wherein the electrocardiogramsegments each have a length of less than 15 seconds and are sampled at asampling frequency in the range of 100 to 600 samples per second.
 23. Amethod as recited in claim 21 wherein the received electrocardiogramsegments are raw electrocardiogram segments and no features of featuresof the electrocardiogram segments are identified to the neural networkduring the training.
 24. A method as recited in claim 21 wherein theneural network includes at least two hidden layers and no more than sixhidden layers.
 25. A method as recited in claim 24 wherein each layerincludes in the range of 8 to 32 filters and each filter has a filtersize in the range of 3-16 and a stride rate in the range of 1-5.